Preferential Solvation of the Antioxidant Agent Daidzein in some Aqueous Co-Solvent Mixtures according to IKBI and QLQC Methods
Bibliographic record
Abstract
The preferential solvation parameters by ethanol (EtOH) or propylene glycol (PG) of daidzein were derived from their solution thermodynamic properties by means of the inverse Kirkwood-Buff integrals and the quasi-lattice-quasi-chemical (QLQC) methods. According to IKBI method, the preferential solvation parameter by the co-solvent, δx1,3, is negative in water-rich mixtures but positive in co-solvent-rich mixtures in both kinds of systems. This could demonstrate the relevant role of hydrophobic hydration around the aromatic rings in the drug solvation in water-rich mixtures. Furthermore, the more solvation by co-solvent in co-solvent-rich mixtures could be due mainly to polarity effects and acidic behavior of the hydroxyl groups of the compound in front to the more basic solvents present in the mixtures, i.e. EtOH or PG. Otherwise, according to QLQC method, this drug is preferentially solvated by the co-solvents in all the mixtures in both kind of systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".